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# ECON41820

#### Econometrics (ECON41820)

Subject:
Economics
College:
Social Sciences & Law
School:
Economics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Tiziana Brancaccio
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No

Curricular information is subject to change.

This is a post-graduate (Masters) level course in econometrics. We will cover estimaton and testing of the general linear regression model, including departures from the classical conditions of exogeneous regressors and spherical errors. We then consider the method of maximum likelihood with some of its applications.

###### Learning Outcomes:

Understanding and using econometric techniques at a masters levels.

###### Indicative Module Content:

1. Linear Regression (Ch. 2)
- model, OLS estimator
- Gauss-Markov assumptions, small sample properties, hypothesis testing
- asymptotic properties

2. More on the Linear Model (Ch. 2-3)
- missing data, outliers
- multicollinearity
- selecting regressors
- selecting functional form

3. Heteroskedasticity (Ch. 4)

4. Autocorrelation (Ch. 4)

5. Endogeneity (Ch. 5)
- Instrumental Variables estimator
- 2-Stage-Least-Squares and Generalized IV estimator
- Generalized Method of Moments

6. Maximum Likelihood (Ch. 6)
- introduction and computational issues
- specification tests: LR, Wald and LM tests
- tests for: omitted variables, heteroskedasticity and autocorrelation

###### Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

100

Lectures

30

Computer Aided Lab

20

Total

150

###### Approaches to Teaching and Learning:
The modules comprises lectures and hands-on computer lab sessions; the latter allow students to apply the techniques learned on real data and to develop confidence in handling datasets and statistical software.
Requirements, Exclusions and Recommendations
Learning Requirements:

Students must have a sound knowledge of matrix algebra and basic statistical concepts (random variables, expectation, common probability distribution - normal, chi square, t and F, joint distributions, point estimation and inference, interval estimation).

Module Requisites and Incompatibles
Not applicable to this module.

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Students will be assigned data to analyse & write-up. They may, if they choose, work in groups of up to two (2) people. Week 11 Graded No

20

No
Quizzes/Short Exercises: Computer-based midterm exam. Exam wil comprise theory questions and empirical analysis of a dataset. Week 9 Alternative linear conversion grade scale 40% No

25

No
Quizzes/Short Exercises: Computer lab test: students will be given a dataset and asked to perform empirical analysis. Week 12 Alternative linear conversion grade scale 40% No

25

No
Exam (In-person): Final Examination End of trimester
Duration:
2 hr(s)
Alternative linear conversion grade scale 40% No

30

No

Carry forward of passed components
No

Resit In Terminal Exam
Summer No